Recent advances in full-head reconstruction have been obtained by optimizing
a neural field through differentiable surface or volume rendering to represent
a single scene. While these techniques achieve an unprecedented accuracy, they
take several minutes, or even hours, due to the expensive optimization process
required. In this work, we introduce InstantAvatar, a method that recovers
full-head avatars from few images (down to just one) in a few seconds on
commodity hardware. In order to speed up the reconstruction process, we propose
a system that combines, for the first time, a voxel-grid neural field
representation with a surface renderer. Notably, a naive combination of these
two techniques leads to unstable optimizations that do not converge to valid
solutions. In order to overcome this limitation, we present a novel statistical
model that learns a prior distribution over 3D head signed distance functions
using a voxel-grid based architecture. The use of this prior model, in
combination with other design choices, results into a system that achieves 3D
head reconstructions with comparable accuracy as the state-of-the-art with a
100x speed-up.

通过组合三维头部重建、神经场和表面渲染技术，本研究提出了一种新的方法（InstantAvatar），可以在几秒钟内从少量图像（甚至只有一张）中恢复出完整的头部化身，并实现了 100 倍的重建速度提升。